Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 243,891 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 243,881 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 0
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-03-01 London 0
## 198 2020-03-02 London 0
## 199 2020-03-03 London 0
## 200 2020-03-04 London 0
## 201 2020-03-05 London 0
## 202 2020-03-06 London 1
## 203 2020-03-07 London 0
## 204 2020-03-08 London 0
## 205 2020-03-09 London 1
## 206 2020-03-10 London 0
## 207 2020-03-11 London 5
## 208 2020-03-12 London 6
## 209 2020-03-13 London 10
## 210 2020-03-14 London 13
## 211 2020-03-15 London 9
## 212 2020-03-16 London 15
## 213 2020-03-17 London 23
## 214 2020-03-18 London 28
## 215 2020-03-19 London 25
## 216 2020-03-20 London 44
## 217 2020-03-21 London 49
## 218 2020-03-22 London 54
## 219 2020-03-23 London 63
## 220 2020-03-24 London 86
## 221 2020-03-25 London 112
## 222 2020-03-26 London 129
## 223 2020-03-27 London 129
## 224 2020-03-28 London 122
## 225 2020-03-29 London 145
## 226 2020-03-30 London 149
## 227 2020-03-31 London 181
## 228 2020-04-01 London 202
## 229 2020-04-02 London 191
## 230 2020-04-03 London 198
## 231 2020-04-04 London 231
## 232 2020-04-05 London 195
## 233 2020-04-06 London 197
## 234 2020-04-07 London 220
## 235 2020-04-08 London 239
## 236 2020-04-09 London 206
## 237 2020-04-10 London 171
## 238 2020-04-11 London 178
## 239 2020-04-12 London 158
## 240 2020-04-13 London 166
## 241 2020-04-14 London 143
## 242 2020-04-15 London 142
## 243 2020-04-16 London 140
## 244 2020-04-17 London 100
## 245 2020-04-18 London 101
## 246 2020-04-19 London 103
## 247 2020-04-20 London 96
## 248 2020-04-21 London 94
## 249 2020-04-22 London 109
## 250 2020-04-23 London 77
## 251 2020-04-24 London 71
## 252 2020-04-25 London 58
## 253 2020-04-26 London 53
## 254 2020-04-27 London 51
## 255 2020-04-28 London 44
## 256 2020-04-29 London 45
## 257 2020-04-30 London 40
## 258 2020-05-01 London 41
## 259 2020-05-02 London 41
## 260 2020-05-03 London 36
## 261 2020-05-04 London 30
## 262 2020-05-05 London 25
## 263 2020-05-06 London 37
## 264 2020-05-07 London 37
## 265 2020-05-08 London 30
## 266 2020-05-09 London 23
## 267 2020-05-10 London 26
## 268 2020-05-11 London 18
## 269 2020-05-12 London 18
## 270 2020-05-13 London 17
## 271 2020-05-14 London 20
## 272 2020-05-15 London 19
## 273 2020-05-16 London 14
## 274 2020-05-17 London 15
## 275 2020-05-18 London 11
## 276 2020-05-19 London 14
## 277 2020-05-20 London 19
## 278 2020-05-21 London 12
## 279 2020-05-22 London 10
## 280 2020-05-23 London 6
## 281 2020-05-24 London 7
## 282 2020-05-25 London 9
## 283 2020-05-26 London 14
## 284 2020-05-27 London 7
## 285 2020-05-28 London 8
## 286 2020-05-29 London 7
## 287 2020-05-30 London 12
## 288 2020-05-31 London 6
## 289 2020-06-01 London 10
## 290 2020-06-02 London 8
## 291 2020-06-03 London 6
## 292 2020-06-04 London 8
## 293 2020-06-05 London 4
## 294 2020-06-06 London 0
## 295 2020-06-07 London 5
## 296 2020-06-08 London 5
## 297 2020-06-09 London 5
## 298 2020-06-10 London 8
## 299 2020-06-11 London 5
## 300 2020-06-12 London 3
## 301 2020-06-13 London 3
## 302 2020-06-14 London 3
## 303 2020-06-15 London 1
## 304 2020-06-16 London 2
## 305 2020-06-17 London 1
## 306 2020-06-18 London 2
## 307 2020-06-19 London 5
## 308 2020-06-20 London 3
## 309 2020-06-21 London 4
## 310 2020-06-22 London 2
## 311 2020-06-23 London 1
## 312 2020-06-24 London 4
## 313 2020-06-25 London 3
## 314 2020-06-26 London 2
## 315 2020-06-27 London 1
## 316 2020-06-28 London 2
## 317 2020-06-29 London 2
## 318 2020-06-30 London 1
## 319 2020-07-01 London 2
## 320 2020-07-02 London 2
## 321 2020-07-03 London 2
## 322 2020-07-04 London 1
## 323 2020-07-05 London 3
## 324 2020-07-06 London 2
## 325 2020-07-07 London 1
## 326 2020-07-08 London 3
## 327 2020-07-09 London 4
## 328 2020-07-10 London 0
## 329 2020-07-11 London 1
## 330 2020-07-12 London 1
## 331 2020-07-13 London 1
## 332 2020-07-14 London 0
## 333 2020-07-15 London 2
## 334 2020-07-16 London 0
## 335 2020-07-17 London 0
## 336 2020-07-18 London 2
## 337 2020-07-19 London 0
## 338 2020-07-20 London 0
## 339 2020-07-21 London 1
## 340 2020-07-22 London 0
## 341 2020-07-23 London 2
## 342 2020-07-24 London 0
## 343 2020-07-25 London 1
## 344 2020-07-26 London 0
## 345 2020-07-27 London 1
## 346 2020-07-28 London 0
## 347 2020-07-29 London 0
## 348 2020-07-30 London 1
## 349 2020-07-31 London 0
## 350 2020-08-01 London 0
## 351 2020-08-02 London 3
## 352 2020-08-03 London 0
## 353 2020-08-04 London 0
## 354 2020-08-05 London 0
## 355 2020-08-06 London 1
## 356 2020-08-07 London 0
## 357 2020-08-08 London 0
## 358 2020-08-09 London 0
## 359 2020-08-10 London 0
## 360 2020-08-11 London 1
## 361 2020-08-12 London 0
## 362 2020-08-13 London 2
## 363 2020-08-14 London 0
## 364 2020-08-15 London 0
## 365 2020-08-16 London 0
## 366 2020-08-17 London 1
## 367 2020-08-18 London 1
## 368 2020-08-19 London 0
## 369 2020-08-20 London 1
## 370 2020-08-21 London 0
## 371 2020-08-22 London 0
## 372 2020-08-23 London 0
## 373 2020-08-24 London 1
## 374 2020-08-25 London 1
## 375 2020-08-26 London 0
## 376 2020-08-27 London 0
## 377 2020-08-28 London 0
## 378 2020-08-29 London 0
## 379 2020-08-30 London 0
## 380 2020-08-31 London 1
## 381 2020-09-01 London 0
## 382 2020-09-02 London 1
## 383 2020-09-03 London 1
## 384 2020-09-04 London 0
## 385 2020-09-05 London 0
## 386 2020-09-06 London 2
## 387 2020-09-07 London 0
## 388 2020-09-08 London 0
## 389 2020-09-09 London 0
## 390 2020-09-10 London 1
## 391 2020-09-11 London 0
## 392 2020-09-12 London 0
## 393 2020-03-01 Midlands 0
## 394 2020-03-02 Midlands 0
## 395 2020-03-03 Midlands 1
## 396 2020-03-04 Midlands 0
## 397 2020-03-05 Midlands 0
## 398 2020-03-06 Midlands 0
## 399 2020-03-07 Midlands 0
## 400 2020-03-08 Midlands 2
## 401 2020-03-09 Midlands 1
## 402 2020-03-10 Midlands 0
## 403 2020-03-11 Midlands 2
## 404 2020-03-12 Midlands 6
## 405 2020-03-13 Midlands 5
## 406 2020-03-14 Midlands 4
## 407 2020-03-15 Midlands 5
## 408 2020-03-16 Midlands 11
## 409 2020-03-17 Midlands 8
## 410 2020-03-18 Midlands 13
## 411 2020-03-19 Midlands 8
## 412 2020-03-20 Midlands 28
## 413 2020-03-21 Midlands 13
## 414 2020-03-22 Midlands 31
## 415 2020-03-23 Midlands 33
## 416 2020-03-24 Midlands 41
## 417 2020-03-25 Midlands 48
## 418 2020-03-26 Midlands 64
## 419 2020-03-27 Midlands 72
## 420 2020-03-28 Midlands 89
## 421 2020-03-29 Midlands 92
## 422 2020-03-30 Midlands 90
## 423 2020-03-31 Midlands 123
## 424 2020-04-01 Midlands 140
## 425 2020-04-02 Midlands 142
## 426 2020-04-03 Midlands 124
## 427 2020-04-04 Midlands 151
## 428 2020-04-05 Midlands 164
## 429 2020-04-06 Midlands 140
## 430 2020-04-07 Midlands 123
## 431 2020-04-08 Midlands 186
## 432 2020-04-09 Midlands 139
## 433 2020-04-10 Midlands 127
## 434 2020-04-11 Midlands 142
## 435 2020-04-12 Midlands 139
## 436 2020-04-13 Midlands 120
## 437 2020-04-14 Midlands 116
## 438 2020-04-15 Midlands 147
## 439 2020-04-16 Midlands 102
## 440 2020-04-17 Midlands 118
## 441 2020-04-18 Midlands 115
## 442 2020-04-19 Midlands 92
## 443 2020-04-20 Midlands 107
## 444 2020-04-21 Midlands 86
## 445 2020-04-22 Midlands 78
## 446 2020-04-23 Midlands 103
## 447 2020-04-24 Midlands 79
## 448 2020-04-25 Midlands 72
## 449 2020-04-26 Midlands 81
## 450 2020-04-27 Midlands 74
## 451 2020-04-28 Midlands 68
## 452 2020-04-29 Midlands 53
## 453 2020-04-30 Midlands 56
## 454 2020-05-01 Midlands 64
## 455 2020-05-02 Midlands 51
## 456 2020-05-03 Midlands 52
## 457 2020-05-04 Midlands 61
## 458 2020-05-05 Midlands 59
## 459 2020-05-06 Midlands 59
## 460 2020-05-07 Midlands 48
## 461 2020-05-08 Midlands 34
## 462 2020-05-09 Midlands 37
## 463 2020-05-10 Midlands 42
## 464 2020-05-11 Midlands 33
## 465 2020-05-12 Midlands 45
## 466 2020-05-13 Midlands 40
## 467 2020-05-14 Midlands 38
## 468 2020-05-15 Midlands 40
## 469 2020-05-16 Midlands 34
## 470 2020-05-17 Midlands 31
## 471 2020-05-18 Midlands 36
## 472 2020-05-19 Midlands 35
## 473 2020-05-20 Midlands 36
## 474 2020-05-21 Midlands 32
## 475 2020-05-22 Midlands 27
## 476 2020-05-23 Midlands 34
## 477 2020-05-24 Midlands 20
## 478 2020-05-25 Midlands 26
## 479 2020-05-26 Midlands 33
## 480 2020-05-27 Midlands 29
## 481 2020-05-28 Midlands 28
## 482 2020-05-29 Midlands 20
## 483 2020-05-30 Midlands 21
## 484 2020-05-31 Midlands 22
## 485 2020-06-01 Midlands 20
## 486 2020-06-02 Midlands 22
## 487 2020-06-03 Midlands 24
## 488 2020-06-04 Midlands 16
## 489 2020-06-05 Midlands 21
## 490 2020-06-06 Midlands 20
## 491 2020-06-07 Midlands 17
## 492 2020-06-08 Midlands 16
## 493 2020-06-09 Midlands 18
## 494 2020-06-10 Midlands 15
## 495 2020-06-11 Midlands 13
## 496 2020-06-12 Midlands 12
## 497 2020-06-13 Midlands 6
## 498 2020-06-14 Midlands 18
## 499 2020-06-15 Midlands 12
## 500 2020-06-16 Midlands 15
## 501 2020-06-17 Midlands 11
## 502 2020-06-18 Midlands 15
## 503 2020-06-19 Midlands 10
## 504 2020-06-20 Midlands 15
## 505 2020-06-21 Midlands 14
## 506 2020-06-22 Midlands 14
## 507 2020-06-23 Midlands 16
## 508 2020-06-24 Midlands 15
## 509 2020-06-25 Midlands 18
## 510 2020-06-26 Midlands 5
## 511 2020-06-27 Midlands 5
## 512 2020-06-28 Midlands 7
## 513 2020-06-29 Midlands 6
## 514 2020-06-30 Midlands 6
## 515 2020-07-01 Midlands 7
## 516 2020-07-02 Midlands 10
## 517 2020-07-03 Midlands 3
## 518 2020-07-04 Midlands 4
## 519 2020-07-05 Midlands 6
## 520 2020-07-06 Midlands 5
## 521 2020-07-07 Midlands 3
## 522 2020-07-08 Midlands 5
## 523 2020-07-09 Midlands 9
## 524 2020-07-10 Midlands 3
## 525 2020-07-11 Midlands 0
## 526 2020-07-12 Midlands 5
## 527 2020-07-13 Midlands 1
## 528 2020-07-14 Midlands 1
## 529 2020-07-15 Midlands 6
## 530 2020-07-16 Midlands 2
## 531 2020-07-17 Midlands 3
## 532 2020-07-18 Midlands 3
## 533 2020-07-19 Midlands 3
## 534 2020-07-20 Midlands 3
## 535 2020-07-21 Midlands 1
## 536 2020-07-22 Midlands 2
## 537 2020-07-23 Midlands 6
## 538 2020-07-24 Midlands 1
## 539 2020-07-25 Midlands 4
## 540 2020-07-26 Midlands 4
## 541 2020-07-27 Midlands 5
## 542 2020-07-28 Midlands 1
## 543 2020-07-29 Midlands 1
## 544 2020-07-30 Midlands 1
## 545 2020-07-31 Midlands 2
## 546 2020-08-01 Midlands 0
## 547 2020-08-02 Midlands 1
## 548 2020-08-03 Midlands 2
## 549 2020-08-04 Midlands 1
## 550 2020-08-05 Midlands 1
## 551 2020-08-06 Midlands 0
## 552 2020-08-07 Midlands 3
## 553 2020-08-08 Midlands 2
## 554 2020-08-09 Midlands 0
## 555 2020-08-10 Midlands 0
## 556 2020-08-11 Midlands 2
## 557 2020-08-12 Midlands 0
## 558 2020-08-13 Midlands 0
## 559 2020-08-14 Midlands 0
## 560 2020-08-15 Midlands 1
## 561 2020-08-16 Midlands 0
## 562 2020-08-17 Midlands 0
## 563 2020-08-18 Midlands 0
## 564 2020-08-19 Midlands 0
## 565 2020-08-20 Midlands 0
## 566 2020-08-21 Midlands 1
## 567 2020-08-22 Midlands 0
## 568 2020-08-23 Midlands 0
## 569 2020-08-24 Midlands 0
## 570 2020-08-25 Midlands 2
## 571 2020-08-26 Midlands 3
## 572 2020-08-27 Midlands 2
## 573 2020-08-28 Midlands 1
## 574 2020-08-29 Midlands 0
## 575 2020-08-30 Midlands 2
## 576 2020-08-31 Midlands 1
## 577 2020-09-01 Midlands 0
## 578 2020-09-02 Midlands 2
## 579 2020-09-03 Midlands 0
## 580 2020-09-04 Midlands 0
## 581 2020-09-05 Midlands 0
## 582 2020-09-06 Midlands 1
## 583 2020-09-07 Midlands 1
## 584 2020-09-08 Midlands 3
## 585 2020-09-09 Midlands 0
## 586 2020-09-10 Midlands 1
## 587 2020-09-11 Midlands 1
## 588 2020-09-12 Midlands 2
## 589 2020-03-01 North East and Yorkshire 0
## 590 2020-03-02 North East and Yorkshire 0
## 591 2020-03-03 North East and Yorkshire 0
## 592 2020-03-04 North East and Yorkshire 0
## 593 2020-03-05 North East and Yorkshire 0
## 594 2020-03-06 North East and Yorkshire 0
## 595 2020-03-07 North East and Yorkshire 0
## 596 2020-03-08 North East and Yorkshire 0
## 597 2020-03-09 North East and Yorkshire 0
## 598 2020-03-10 North East and Yorkshire 0
## 599 2020-03-11 North East and Yorkshire 0
## 600 2020-03-12 North East and Yorkshire 0
## 601 2020-03-13 North East and Yorkshire 0
## 602 2020-03-14 North East and Yorkshire 0
## 603 2020-03-15 North East and Yorkshire 2
## 604 2020-03-16 North East and Yorkshire 3
## 605 2020-03-17 North East and Yorkshire 1
## 606 2020-03-18 North East and Yorkshire 2
## 607 2020-03-19 North East and Yorkshire 6
## 608 2020-03-20 North East and Yorkshire 5
## 609 2020-03-21 North East and Yorkshire 6
## 610 2020-03-22 North East and Yorkshire 7
## 611 2020-03-23 North East and Yorkshire 9
## 612 2020-03-24 North East and Yorkshire 8
## 613 2020-03-25 North East and Yorkshire 18
## 614 2020-03-26 North East and Yorkshire 21
## 615 2020-03-27 North East and Yorkshire 28
## 616 2020-03-28 North East and Yorkshire 35
## 617 2020-03-29 North East and Yorkshire 38
## 618 2020-03-30 North East and Yorkshire 64
## 619 2020-03-31 North East and Yorkshire 60
## 620 2020-04-01 North East and Yorkshire 67
## 621 2020-04-02 North East and Yorkshire 75
## 622 2020-04-03 North East and Yorkshire 100
## 623 2020-04-04 North East and Yorkshire 105
## 624 2020-04-05 North East and Yorkshire 92
## 625 2020-04-06 North East and Yorkshire 96
## 626 2020-04-07 North East and Yorkshire 102
## 627 2020-04-08 North East and Yorkshire 107
## 628 2020-04-09 North East and Yorkshire 111
## 629 2020-04-10 North East and Yorkshire 117
## 630 2020-04-11 North East and Yorkshire 98
## 631 2020-04-12 North East and Yorkshire 84
## 632 2020-04-13 North East and Yorkshire 94
## 633 2020-04-14 North East and Yorkshire 107
## 634 2020-04-15 North East and Yorkshire 96
## 635 2020-04-16 North East and Yorkshire 103
## 636 2020-04-17 North East and Yorkshire 88
## 637 2020-04-18 North East and Yorkshire 95
## 638 2020-04-19 North East and Yorkshire 88
## 639 2020-04-20 North East and Yorkshire 100
## 640 2020-04-21 North East and Yorkshire 76
## 641 2020-04-22 North East and Yorkshire 84
## 642 2020-04-23 North East and Yorkshire 63
## 643 2020-04-24 North East and Yorkshire 72
## 644 2020-04-25 North East and Yorkshire 69
## 645 2020-04-26 North East and Yorkshire 65
## 646 2020-04-27 North East and Yorkshire 65
## 647 2020-04-28 North East and Yorkshire 57
## 648 2020-04-29 North East and Yorkshire 69
## 649 2020-04-30 North East and Yorkshire 57
## 650 2020-05-01 North East and Yorkshire 64
## 651 2020-05-02 North East and Yorkshire 48
## 652 2020-05-03 North East and Yorkshire 40
## 653 2020-05-04 North East and Yorkshire 49
## 654 2020-05-05 North East and Yorkshire 40
## 655 2020-05-06 North East and Yorkshire 51
## 656 2020-05-07 North East and Yorkshire 45
## 657 2020-05-08 North East and Yorkshire 42
## 658 2020-05-09 North East and Yorkshire 44
## 659 2020-05-10 North East and Yorkshire 40
## 660 2020-05-11 North East and Yorkshire 29
## 661 2020-05-12 North East and Yorkshire 27
## 662 2020-05-13 North East and Yorkshire 28
## 663 2020-05-14 North East and Yorkshire 31
## 664 2020-05-15 North East and Yorkshire 32
## 665 2020-05-16 North East and Yorkshire 35
## 666 2020-05-17 North East and Yorkshire 26
## 667 2020-05-18 North East and Yorkshire 30
## 668 2020-05-19 North East and Yorkshire 27
## 669 2020-05-20 North East and Yorkshire 22
## 670 2020-05-21 North East and Yorkshire 33
## 671 2020-05-22 North East and Yorkshire 22
## 672 2020-05-23 North East and Yorkshire 18
## 673 2020-05-24 North East and Yorkshire 26
## 674 2020-05-25 North East and Yorkshire 21
## 675 2020-05-26 North East and Yorkshire 21
## 676 2020-05-27 North East and Yorkshire 22
## 677 2020-05-28 North East and Yorkshire 21
## 678 2020-05-29 North East and Yorkshire 25
## 679 2020-05-30 North East and Yorkshire 20
## 680 2020-05-31 North East and Yorkshire 20
## 681 2020-06-01 North East and Yorkshire 17
## 682 2020-06-02 North East and Yorkshire 23
## 683 2020-06-03 North East and Yorkshire 23
## 684 2020-06-04 North East and Yorkshire 17
## 685 2020-06-05 North East and Yorkshire 18
## 686 2020-06-06 North East and Yorkshire 21
## 687 2020-06-07 North East and Yorkshire 14
## 688 2020-06-08 North East and Yorkshire 11
## 689 2020-06-09 North East and Yorkshire 12
## 690 2020-06-10 North East and Yorkshire 19
## 691 2020-06-11 North East and Yorkshire 7
## 692 2020-06-12 North East and Yorkshire 9
## 693 2020-06-13 North East and Yorkshire 10
## 694 2020-06-14 North East and Yorkshire 11
## 695 2020-06-15 North East and Yorkshire 9
## 696 2020-06-16 North East and Yorkshire 10
## 697 2020-06-17 North East and Yorkshire 9
## 698 2020-06-18 North East and Yorkshire 11
## 699 2020-06-19 North East and Yorkshire 6
## 700 2020-06-20 North East and Yorkshire 5
## 701 2020-06-21 North East and Yorkshire 4
## 702 2020-06-22 North East and Yorkshire 7
## 703 2020-06-23 North East and Yorkshire 8
## 704 2020-06-24 North East and Yorkshire 10
## 705 2020-06-25 North East and Yorkshire 4
## 706 2020-06-26 North East and Yorkshire 8
## 707 2020-06-27 North East and Yorkshire 4
## 708 2020-06-28 North East and Yorkshire 5
## 709 2020-06-29 North East and Yorkshire 2
## 710 2020-06-30 North East and Yorkshire 7
## 711 2020-07-01 North East and Yorkshire 1
## 712 2020-07-02 North East and Yorkshire 4
## 713 2020-07-03 North East and Yorkshire 4
## 714 2020-07-04 North East and Yorkshire 4
## 715 2020-07-05 North East and Yorkshire 3
## 716 2020-07-06 North East and Yorkshire 2
## 717 2020-07-07 North East and Yorkshire 3
## 718 2020-07-08 North East and Yorkshire 3
## 719 2020-07-09 North East and Yorkshire 0
## 720 2020-07-10 North East and Yorkshire 3
## 721 2020-07-11 North East and Yorkshire 1
## 722 2020-07-12 North East and Yorkshire 4
## 723 2020-07-13 North East and Yorkshire 1
## 724 2020-07-14 North East and Yorkshire 1
## 725 2020-07-15 North East and Yorkshire 2
## 726 2020-07-16 North East and Yorkshire 3
## 727 2020-07-17 North East and Yorkshire 1
## 728 2020-07-18 North East and Yorkshire 2
## 729 2020-07-19 North East and Yorkshire 2
## 730 2020-07-20 North East and Yorkshire 1
## 731 2020-07-21 North East and Yorkshire 1
## 732 2020-07-22 North East and Yorkshire 6
## 733 2020-07-23 North East and Yorkshire 0
## 734 2020-07-24 North East and Yorkshire 1
## 735 2020-07-25 North East and Yorkshire 5
## 736 2020-07-26 North East and Yorkshire 1
## 737 2020-07-27 North East and Yorkshire 0
## 738 2020-07-28 North East and Yorkshire 2
## 739 2020-07-29 North East and Yorkshire 1
## 740 2020-07-30 North East and Yorkshire 0
## 741 2020-07-31 North East and Yorkshire 1
## 742 2020-08-01 North East and Yorkshire 3
## 743 2020-08-02 North East and Yorkshire 2
## 744 2020-08-03 North East and Yorkshire 1
## 745 2020-08-04 North East and Yorkshire 2
## 746 2020-08-05 North East and Yorkshire 1
## 747 2020-08-06 North East and Yorkshire 4
## 748 2020-08-07 North East and Yorkshire 0
## 749 2020-08-08 North East and Yorkshire 1
## 750 2020-08-09 North East and Yorkshire 2
## 751 2020-08-10 North East and Yorkshire 2
## 752 2020-08-11 North East and Yorkshire 2
## 753 2020-08-12 North East and Yorkshire 2
## 754 2020-08-13 North East and Yorkshire 0
## 755 2020-08-14 North East and Yorkshire 1
## 756 2020-08-15 North East and Yorkshire 1
## 757 2020-08-16 North East and Yorkshire 0
## 758 2020-08-17 North East and Yorkshire 4
## 759 2020-08-18 North East and Yorkshire 1
## 760 2020-08-19 North East and Yorkshire 0
## 761 2020-08-20 North East and Yorkshire 0
## 762 2020-08-21 North East and Yorkshire 1
## 763 2020-08-22 North East and Yorkshire 1
## 764 2020-08-23 North East and Yorkshire 2
## 765 2020-08-24 North East and Yorkshire 0
## 766 2020-08-25 North East and Yorkshire 1
## 767 2020-08-26 North East and Yorkshire 2
## 768 2020-08-27 North East and Yorkshire 1
## 769 2020-08-28 North East and Yorkshire 0
## 770 2020-08-29 North East and Yorkshire 1
## 771 2020-08-30 North East and Yorkshire 0
## 772 2020-08-31 North East and Yorkshire 0
## 773 2020-09-01 North East and Yorkshire 2
## 774 2020-09-02 North East and Yorkshire 3
## 775 2020-09-03 North East and Yorkshire 0
## 776 2020-09-04 North East and Yorkshire 0
## 777 2020-09-05 North East and Yorkshire 2
## 778 2020-09-06 North East and Yorkshire 1
## 779 2020-09-07 North East and Yorkshire 0
## 780 2020-09-08 North East and Yorkshire 1
## 781 2020-09-09 North East and Yorkshire 1
## 782 2020-09-10 North East and Yorkshire 0
## 783 2020-09-11 North East and Yorkshire 3
## 784 2020-09-12 North East and Yorkshire 0
## 785 2020-03-01 North West 0
## 786 2020-03-02 North West 0
## 787 2020-03-03 North West 0
## 788 2020-03-04 North West 0
## 789 2020-03-05 North West 1
## 790 2020-03-06 North West 0
## 791 2020-03-07 North West 0
## 792 2020-03-08 North West 1
## 793 2020-03-09 North West 0
## 794 2020-03-10 North West 0
## 795 2020-03-11 North West 0
## 796 2020-03-12 North West 2
## 797 2020-03-13 North West 3
## 798 2020-03-14 North West 1
## 799 2020-03-15 North West 4
## 800 2020-03-16 North West 2
## 801 2020-03-17 North West 4
## 802 2020-03-18 North West 6
## 803 2020-03-19 North West 7
## 804 2020-03-20 North West 10
## 805 2020-03-21 North West 11
## 806 2020-03-22 North West 13
## 807 2020-03-23 North West 15
## 808 2020-03-24 North West 21
## 809 2020-03-25 North West 21
## 810 2020-03-26 North West 29
## 811 2020-03-27 North West 36
## 812 2020-03-28 North West 28
## 813 2020-03-29 North West 46
## 814 2020-03-30 North West 67
## 815 2020-03-31 North West 52
## 816 2020-04-01 North West 86
## 817 2020-04-02 North West 96
## 818 2020-04-03 North West 95
## 819 2020-04-04 North West 98
## 820 2020-04-05 North West 102
## 821 2020-04-06 North West 100
## 822 2020-04-07 North West 135
## 823 2020-04-08 North West 127
## 824 2020-04-09 North West 119
## 825 2020-04-10 North West 117
## 826 2020-04-11 North West 138
## 827 2020-04-12 North West 125
## 828 2020-04-13 North West 129
## 829 2020-04-14 North West 131
## 830 2020-04-15 North West 114
## 831 2020-04-16 North West 135
## 832 2020-04-17 North West 98
## 833 2020-04-18 North West 113
## 834 2020-04-19 North West 71
## 835 2020-04-20 North West 83
## 836 2020-04-21 North West 76
## 837 2020-04-22 North West 86
## 838 2020-04-23 North West 85
## 839 2020-04-24 North West 66
## 840 2020-04-25 North West 66
## 841 2020-04-26 North West 55
## 842 2020-04-27 North West 54
## 843 2020-04-28 North West 57
## 844 2020-04-29 North West 63
## 845 2020-04-30 North West 59
## 846 2020-05-01 North West 45
## 847 2020-05-02 North West 56
## 848 2020-05-03 North West 55
## 849 2020-05-04 North West 48
## 850 2020-05-05 North West 48
## 851 2020-05-06 North West 44
## 852 2020-05-07 North West 49
## 853 2020-05-08 North West 42
## 854 2020-05-09 North West 31
## 855 2020-05-10 North West 42
## 856 2020-05-11 North West 35
## 857 2020-05-12 North West 38
## 858 2020-05-13 North West 25
## 859 2020-05-14 North West 26
## 860 2020-05-15 North West 33
## 861 2020-05-16 North West 32
## 862 2020-05-17 North West 24
## 863 2020-05-18 North West 31
## 864 2020-05-19 North West 35
## 865 2020-05-20 North West 27
## 866 2020-05-21 North West 27
## 867 2020-05-22 North West 26
## 868 2020-05-23 North West 31
## 869 2020-05-24 North West 26
## 870 2020-05-25 North West 31
## 871 2020-05-26 North West 27
## 872 2020-05-27 North West 27
## 873 2020-05-28 North West 28
## 874 2020-05-29 North West 20
## 875 2020-05-30 North West 19
## 876 2020-05-31 North West 13
## 877 2020-06-01 North West 12
## 878 2020-06-02 North West 27
## 879 2020-06-03 North West 22
## 880 2020-06-04 North West 22
## 881 2020-06-05 North West 16
## 882 2020-06-06 North West 26
## 883 2020-06-07 North West 20
## 884 2020-06-08 North West 23
## 885 2020-06-09 North West 17
## 886 2020-06-10 North West 16
## 887 2020-06-11 North West 16
## 888 2020-06-12 North West 11
## 889 2020-06-13 North West 10
## 890 2020-06-14 North West 15
## 891 2020-06-15 North West 16
## 892 2020-06-16 North West 16
## 893 2020-06-17 North West 13
## 894 2020-06-18 North West 14
## 895 2020-06-19 North West 7
## 896 2020-06-20 North West 11
## 897 2020-06-21 North West 8
## 898 2020-06-22 North West 11
## 899 2020-06-23 North West 13
## 900 2020-06-24 North West 13
## 901 2020-06-25 North West 15
## 902 2020-06-26 North West 6
## 903 2020-06-27 North West 7
## 904 2020-06-28 North West 9
## 905 2020-06-29 North West 9
## 906 2020-06-30 North West 7
## 907 2020-07-01 North West 3
## 908 2020-07-02 North West 6
## 909 2020-07-03 North West 7
## 910 2020-07-04 North West 4
## 911 2020-07-05 North West 6
## 912 2020-07-06 North West 9
## 913 2020-07-07 North West 8
## 914 2020-07-08 North West 5
## 915 2020-07-09 North West 10
## 916 2020-07-10 North West 2
## 917 2020-07-11 North West 5
## 918 2020-07-12 North West 0
## 919 2020-07-13 North West 6
## 920 2020-07-14 North West 4
## 921 2020-07-15 North West 5
## 922 2020-07-16 North West 2
## 923 2020-07-17 North West 4
## 924 2020-07-18 North West 5
## 925 2020-07-19 North West 3
## 926 2020-07-20 North West 0
## 927 2020-07-21 North West 2
## 928 2020-07-22 North West 3
## 929 2020-07-23 North West 3
## 930 2020-07-24 North West 1
## 931 2020-07-25 North West 1
## 932 2020-07-26 North West 3
## 933 2020-07-27 North West 1
## 934 2020-07-28 North West 1
## 935 2020-07-29 North West 2
## 936 2020-07-30 North West 2
## 937 2020-07-31 North West 0
## 938 2020-08-01 North West 2
## 939 2020-08-02 North West 1
## 940 2020-08-03 North West 8
## 941 2020-08-04 North West 3
## 942 2020-08-05 North West 2
## 943 2020-08-06 North West 2
## 944 2020-08-07 North West 2
## 945 2020-08-08 North West 2
## 946 2020-08-09 North West 3
## 947 2020-08-10 North West 2
## 948 2020-08-11 North West 3
## 949 2020-08-12 North West 0
## 950 2020-08-13 North West 2
## 951 2020-08-14 North West 2
## 952 2020-08-15 North West 6
## 953 2020-08-16 North West 2
## 954 2020-08-17 North West 1
## 955 2020-08-18 North West 2
## 956 2020-08-19 North West 0
## 957 2020-08-20 North West 1
## 958 2020-08-21 North West 4
## 959 2020-08-22 North West 3
## 960 2020-08-23 North West 4
## 961 2020-08-24 North West 4
## 962 2020-08-25 North West 3
## 963 2020-08-26 North West 4
## 964 2020-08-27 North West 1
## 965 2020-08-28 North West 2
## 966 2020-08-29 North West 0
## 967 2020-08-30 North West 2
## 968 2020-08-31 North West 2
## 969 2020-09-01 North West 0
## 970 2020-09-02 North West 2
## 971 2020-09-03 North West 1
## 972 2020-09-04 North West 3
## 973 2020-09-05 North West 6
## 974 2020-09-06 North West 1
## 975 2020-09-07 North West 8
## 976 2020-09-08 North West 5
## 977 2020-09-09 North West 4
## 978 2020-09-10 North West 4
## 979 2020-09-11 North West 0
## 980 2020-09-12 North West 0
## 981 2020-03-01 South East 0
## 982 2020-03-02 South East 0
## 983 2020-03-03 South East 1
## 984 2020-03-04 South East 0
## 985 2020-03-05 South East 1
## 986 2020-03-06 South East 0
## 987 2020-03-07 South East 0
## 988 2020-03-08 South East 1
## 989 2020-03-09 South East 1
## 990 2020-03-10 South East 1
## 991 2020-03-11 South East 1
## 992 2020-03-12 South East 0
## 993 2020-03-13 South East 1
## 994 2020-03-14 South East 1
## 995 2020-03-15 South East 5
## 996 2020-03-16 South East 8
## 997 2020-03-17 South East 7
## 998 2020-03-18 South East 10
## 999 2020-03-19 South East 9
## 1000 2020-03-20 South East 13
## 1001 2020-03-21 South East 7
## 1002 2020-03-22 South East 25
## 1003 2020-03-23 South East 20
## 1004 2020-03-24 South East 22
## 1005 2020-03-25 South East 29
## 1006 2020-03-26 South East 35
## 1007 2020-03-27 South East 35
## 1008 2020-03-28 South East 36
## 1009 2020-03-29 South East 55
## 1010 2020-03-30 South East 58
## 1011 2020-03-31 South East 65
## 1012 2020-04-01 South East 66
## 1013 2020-04-02 South East 55
## 1014 2020-04-03 South East 72
## 1015 2020-04-04 South East 80
## 1016 2020-04-05 South East 82
## 1017 2020-04-06 South East 88
## 1018 2020-04-07 South East 100
## 1019 2020-04-08 South East 83
## 1020 2020-04-09 South East 104
## 1021 2020-04-10 South East 88
## 1022 2020-04-11 South East 88
## 1023 2020-04-12 South East 88
## 1024 2020-04-13 South East 84
## 1025 2020-04-14 South East 65
## 1026 2020-04-15 South East 72
## 1027 2020-04-16 South East 56
## 1028 2020-04-17 South East 86
## 1029 2020-04-18 South East 57
## 1030 2020-04-19 South East 70
## 1031 2020-04-20 South East 87
## 1032 2020-04-21 South East 51
## 1033 2020-04-22 South East 54
## 1034 2020-04-23 South East 57
## 1035 2020-04-24 South East 64
## 1036 2020-04-25 South East 51
## 1037 2020-04-26 South East 51
## 1038 2020-04-27 South East 41
## 1039 2020-04-28 South East 40
## 1040 2020-04-29 South East 47
## 1041 2020-04-30 South East 29
## 1042 2020-05-01 South East 37
## 1043 2020-05-02 South East 36
## 1044 2020-05-03 South East 17
## 1045 2020-05-04 South East 35
## 1046 2020-05-05 South East 29
## 1047 2020-05-06 South East 25
## 1048 2020-05-07 South East 27
## 1049 2020-05-08 South East 26
## 1050 2020-05-09 South East 28
## 1051 2020-05-10 South East 19
## 1052 2020-05-11 South East 25
## 1053 2020-05-12 South East 27
## 1054 2020-05-13 South East 18
## 1055 2020-05-14 South East 32
## 1056 2020-05-15 South East 25
## 1057 2020-05-16 South East 22
## 1058 2020-05-17 South East 18
## 1059 2020-05-18 South East 22
## 1060 2020-05-19 South East 12
## 1061 2020-05-20 South East 22
## 1062 2020-05-21 South East 15
## 1063 2020-05-22 South East 17
## 1064 2020-05-23 South East 21
## 1065 2020-05-24 South East 17
## 1066 2020-05-25 South East 13
## 1067 2020-05-26 South East 19
## 1068 2020-05-27 South East 19
## 1069 2020-05-28 South East 12
## 1070 2020-05-29 South East 22
## 1071 2020-05-30 South East 8
## 1072 2020-05-31 South East 12
## 1073 2020-06-01 South East 11
## 1074 2020-06-02 South East 13
## 1075 2020-06-03 South East 18
## 1076 2020-06-04 South East 11
## 1077 2020-06-05 South East 11
## 1078 2020-06-06 South East 10
## 1079 2020-06-07 South East 12
## 1080 2020-06-08 South East 8
## 1081 2020-06-09 South East 10
## 1082 2020-06-10 South East 11
## 1083 2020-06-11 South East 5
## 1084 2020-06-12 South East 6
## 1085 2020-06-13 South East 7
## 1086 2020-06-14 South East 7
## 1087 2020-06-15 South East 8
## 1088 2020-06-16 South East 14
## 1089 2020-06-17 South East 9
## 1090 2020-06-18 South East 4
## 1091 2020-06-19 South East 7
## 1092 2020-06-20 South East 5
## 1093 2020-06-21 South East 3
## 1094 2020-06-22 South East 2
## 1095 2020-06-23 South East 9
## 1096 2020-06-24 South East 7
## 1097 2020-06-25 South East 5
## 1098 2020-06-26 South East 8
## 1099 2020-06-27 South East 9
## 1100 2020-06-28 South East 6
## 1101 2020-06-29 South East 5
## 1102 2020-06-30 South East 5
## 1103 2020-07-01 South East 2
## 1104 2020-07-02 South East 8
## 1105 2020-07-03 South East 3
## 1106 2020-07-04 South East 6
## 1107 2020-07-05 South East 5
## 1108 2020-07-06 South East 4
## 1109 2020-07-07 South East 6
## 1110 2020-07-08 South East 3
## 1111 2020-07-09 South East 7
## 1112 2020-07-10 South East 3
## 1113 2020-07-11 South East 4
## 1114 2020-07-12 South East 4
## 1115 2020-07-13 South East 5
## 1116 2020-07-14 South East 5
## 1117 2020-07-15 South East 6
## 1118 2020-07-16 South East 3
## 1119 2020-07-17 South East 1
## 1120 2020-07-18 South East 5
## 1121 2020-07-19 South East 2
## 1122 2020-07-20 South East 6
## 1123 2020-07-21 South East 4
## 1124 2020-07-22 South East 2
## 1125 2020-07-23 South East 3
## 1126 2020-07-24 South East 1
## 1127 2020-07-25 South East 1
## 1128 2020-07-26 South East 3
## 1129 2020-07-27 South East 1
## 1130 2020-07-28 South East 3
## 1131 2020-07-29 South East 2
## 1132 2020-07-30 South East 3
## 1133 2020-07-31 South East 1
## 1134 2020-08-01 South East 2
## 1135 2020-08-02 South East 4
## 1136 2020-08-03 South East 0
## 1137 2020-08-04 South East 0
## 1138 2020-08-05 South East 0
## 1139 2020-08-06 South East 2
## 1140 2020-08-07 South East 0
## 1141 2020-08-08 South East 2
## 1142 2020-08-09 South East 0
## 1143 2020-08-10 South East 2
## 1144 2020-08-11 South East 1
## 1145 2020-08-12 South East 1
## 1146 2020-08-13 South East 0
## 1147 2020-08-14 South East 0
## 1148 2020-08-15 South East 2
## 1149 2020-08-16 South East 1
## 1150 2020-08-17 South East 0
## 1151 2020-08-18 South East 2
## 1152 2020-08-19 South East 1
## 1153 2020-08-20 South East 0
## 1154 2020-08-21 South East 0
## 1155 2020-08-22 South East 0
## 1156 2020-08-23 South East 1
## 1157 2020-08-24 South East 0
## 1158 2020-08-25 South East 1
## 1159 2020-08-26 South East 0
## 1160 2020-08-27 South East 1
## 1161 2020-08-28 South East 1
## 1162 2020-08-29 South East 1
## 1163 2020-08-30 South East 0
## 1164 2020-08-31 South East 2
## 1165 2020-09-01 South East 1
## 1166 2020-09-02 South East 1
## 1167 2020-09-03 South East 0
## 1168 2020-09-04 South East 1
## 1169 2020-09-05 South East 0
## 1170 2020-09-06 South East 1
## 1171 2020-09-07 South East 0
## 1172 2020-09-08 South East 0
## 1173 2020-09-09 South East 0
## 1174 2020-09-10 South East 0
## 1175 2020-09-11 South East 0
## 1176 2020-09-12 South East 0
## 1177 2020-03-01 South West 0
## 1178 2020-03-02 South West 0
## 1179 2020-03-03 South West 0
## 1180 2020-03-04 South West 0
## 1181 2020-03-05 South West 0
## 1182 2020-03-06 South West 0
## 1183 2020-03-07 South West 0
## 1184 2020-03-08 South West 0
## 1185 2020-03-09 South West 0
## 1186 2020-03-10 South West 0
## 1187 2020-03-11 South West 1
## 1188 2020-03-12 South West 0
## 1189 2020-03-13 South West 0
## 1190 2020-03-14 South West 1
## 1191 2020-03-15 South West 0
## 1192 2020-03-16 South West 0
## 1193 2020-03-17 South West 2
## 1194 2020-03-18 South West 2
## 1195 2020-03-19 South West 4
## 1196 2020-03-20 South West 3
## 1197 2020-03-21 South West 6
## 1198 2020-03-22 South West 7
## 1199 2020-03-23 South West 8
## 1200 2020-03-24 South West 7
## 1201 2020-03-25 South West 9
## 1202 2020-03-26 South West 11
## 1203 2020-03-27 South West 13
## 1204 2020-03-28 South West 21
## 1205 2020-03-29 South West 18
## 1206 2020-03-30 South West 23
## 1207 2020-03-31 South West 23
## 1208 2020-04-01 South West 21
## 1209 2020-04-02 South West 23
## 1210 2020-04-03 South West 30
## 1211 2020-04-04 South West 42
## 1212 2020-04-05 South West 32
## 1213 2020-04-06 South West 34
## 1214 2020-04-07 South West 39
## 1215 2020-04-08 South West 47
## 1216 2020-04-09 South West 24
## 1217 2020-04-10 South West 46
## 1218 2020-04-11 South West 43
## 1219 2020-04-12 South West 23
## 1220 2020-04-13 South West 27
## 1221 2020-04-14 South West 24
## 1222 2020-04-15 South West 32
## 1223 2020-04-16 South West 29
## 1224 2020-04-17 South West 33
## 1225 2020-04-18 South West 25
## 1226 2020-04-19 South West 31
## 1227 2020-04-20 South West 26
## 1228 2020-04-21 South West 26
## 1229 2020-04-22 South West 23
## 1230 2020-04-23 South West 17
## 1231 2020-04-24 South West 19
## 1232 2020-04-25 South West 15
## 1233 2020-04-26 South West 27
## 1234 2020-04-27 South West 13
## 1235 2020-04-28 South West 17
## 1236 2020-04-29 South West 15
## 1237 2020-04-30 South West 26
## 1238 2020-05-01 South West 6
## 1239 2020-05-02 South West 7
## 1240 2020-05-03 South West 10
## 1241 2020-05-04 South West 17
## 1242 2020-05-05 South West 14
## 1243 2020-05-06 South West 19
## 1244 2020-05-07 South West 16
## 1245 2020-05-08 South West 6
## 1246 2020-05-09 South West 11
## 1247 2020-05-10 South West 5
## 1248 2020-05-11 South West 8
## 1249 2020-05-12 South West 7
## 1250 2020-05-13 South West 7
## 1251 2020-05-14 South West 6
## 1252 2020-05-15 South West 4
## 1253 2020-05-16 South West 4
## 1254 2020-05-17 South West 6
## 1255 2020-05-18 South West 4
## 1256 2020-05-19 South West 6
## 1257 2020-05-20 South West 1
## 1258 2020-05-21 South West 9
## 1259 2020-05-22 South West 7
## 1260 2020-05-23 South West 6
## 1261 2020-05-24 South West 3
## 1262 2020-05-25 South West 8
## 1263 2020-05-26 South West 11
## 1264 2020-05-27 South West 5
## 1265 2020-05-28 South West 10
## 1266 2020-05-29 South West 7
## 1267 2020-05-30 South West 3
## 1268 2020-05-31 South West 2
## 1269 2020-06-01 South West 7
## 1270 2020-06-02 South West 2
## 1271 2020-06-03 South West 7
## 1272 2020-06-04 South West 2
## 1273 2020-06-05 South West 2
## 1274 2020-06-06 South West 1
## 1275 2020-06-07 South West 3
## 1276 2020-06-08 South West 3
## 1277 2020-06-09 South West 0
## 1278 2020-06-10 South West 1
## 1279 2020-06-11 South West 2
## 1280 2020-06-12 South West 2
## 1281 2020-06-13 South West 2
## 1282 2020-06-14 South West 0
## 1283 2020-06-15 South West 2
## 1284 2020-06-16 South West 2
## 1285 2020-06-17 South West 0
## 1286 2020-06-18 South West 0
## 1287 2020-06-19 South West 0
## 1288 2020-06-20 South West 2
## 1289 2020-06-21 South West 0
## 1290 2020-06-22 South West 1
## 1291 2020-06-23 South West 1
## 1292 2020-06-24 South West 1
## 1293 2020-06-25 South West 0
## 1294 2020-06-26 South West 3
## 1295 2020-06-27 South West 0
## 1296 2020-06-28 South West 0
## 1297 2020-06-29 South West 1
## 1298 2020-06-30 South West 0
## 1299 2020-07-01 South West 0
## 1300 2020-07-02 South West 0
## 1301 2020-07-03 South West 0
## 1302 2020-07-04 South West 0
## 1303 2020-07-05 South West 1
## 1304 2020-07-06 South West 0
## 1305 2020-07-07 South West 0
## 1306 2020-07-08 South West 2
## 1307 2020-07-09 South West 0
## 1308 2020-07-10 South West 1
## 1309 2020-07-11 South West 0
## 1310 2020-07-12 South West 0
## 1311 2020-07-13 South West 1
## 1312 2020-07-14 South West 0
## 1313 2020-07-15 South West 0
## 1314 2020-07-16 South West 0
## 1315 2020-07-17 South West 1
## 1316 2020-07-18 South West 0
## 1317 2020-07-19 South West 0
## 1318 2020-07-20 South West 0
## 1319 2020-07-21 South West 0
## 1320 2020-07-22 South West 0
## 1321 2020-07-23 South West 0
## 1322 2020-07-24 South West 0
## 1323 2020-07-25 South West 0
## 1324 2020-07-26 South West 0
## 1325 2020-07-27 South West 0
## 1326 2020-07-28 South West 0
## 1327 2020-07-29 South West 0
## 1328 2020-07-30 South West 1
## 1329 2020-07-31 South West 0
## 1330 2020-08-01 South West 0
## 1331 2020-08-02 South West 0
## 1332 2020-08-03 South West 0
## 1333 2020-08-04 South West 0
## 1334 2020-08-05 South West 0
## 1335 2020-08-06 South West 0
## 1336 2020-08-07 South West 0
## 1337 2020-08-08 South West 0
## 1338 2020-08-09 South West 0
## 1339 2020-08-10 South West 0
## 1340 2020-08-11 South West 0
## 1341 2020-08-12 South West 0
## 1342 2020-08-13 South West 0
## 1343 2020-08-14 South West 1
## 1344 2020-08-15 South West 0
## 1345 2020-08-16 South West 0
## 1346 2020-08-17 South West 2
## 1347 2020-08-18 South West 0
## 1348 2020-08-19 South West 0
## 1349 2020-08-20 South West 0
## 1350 2020-08-21 South West 0
## 1351 2020-08-22 South West 0
## 1352 2020-08-23 South West 0
## 1353 2020-08-24 South West 0
## 1354 2020-08-25 South West 1
## 1355 2020-08-26 South West 0
## 1356 2020-08-27 South West 1
## 1357 2020-08-28 South West 0
## 1358 2020-08-29 South West 0
## 1359 2020-08-30 South West 0
## 1360 2020-08-31 South West 0
## 1361 2020-09-01 South West 0
## 1362 2020-09-02 South West 0
## 1363 2020-09-03 South West 0
## 1364 2020-09-04 South West 0
## 1365 2020-09-05 South West 0
## 1366 2020-09-06 South West 0
## 1367 2020-09-07 South West 0
## 1368 2020-09-08 South West 1
## 1369 2020-09-09 South West 0
## 1370 2020-09-10 South West 0
## 1371 2020-09-11 South West 0
## 1372 2020-09-12 South West 0We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Sunday 13 Sep 2020.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -19.692 -7.783 -2.744 4.220 13.203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.028e+00 8.306e-02 48.50 <2e-16 ***
## note_lag 1.918e-05 8.625e-07 22.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 49.06532)
##
## Null deviance: 27665.5 on 134 degrees of freedom
## Residual deviance: 7039.7 on 133 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 56.169241 1.000019
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 47.551577 65.863200
## note_lag 1.000017 1.000021
Rsq(lag_mod)
## [1] 0.745543
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64"
## nodename
## "Mac-1600078392216.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.3 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.8-29 cyphr_1.1.0 DT_0.15
## [7] kableExtra_1.2.1 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.2 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_1.3-2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.11 farver_2.0.3 fansi_0.4.1 splines_4.0.2
## [13] knitr_1.29 jsonlite_1.7.1 nloptr_1.2.2.2 broom_0.7.0
## [17] dbplyr_1.4.4 compiler_4.0.2 httr_1.4.2 backports_1.1.9
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.0.2 htmltools_0.5.0
## [25] tools_4.0.2 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.4 nlme_3.1-148
## [33] matchmaker_0.1.1 crosstalk_1.1.0.1 xfun_0.17 openxlsx_4.1.5
## [37] lme4_1.1-23 lifecycle_0.2.0 statmod_1.4.34 rstatix_0.6.0
## [41] MASS_7.3-51.6 scales_1.1.1 hms_0.5.3 sodium_1.1
## [45] yaml_2.2.1 curl_4.3 gridExtra_2.3 stringi_1.5.3
## [49] kyotil_2020.8-22 boot_1.3-25 zip_2.1.1 rlang_0.4.7
## [53] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 htmlwidgets_1.5.1
## [57] labeling_0.3 cowplot_1.1.0 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.6 haven_2.3.1 foreign_0.8-80 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-12 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-9 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_4.0.2 readxl_1.3.1 data.table_1.13.0
## [81] blob_1.2.1 reprex_0.3.0 digest_0.6.25 webshot_0.5.2
## [85] munsell_0.5.0 viridisLite_0.3.0